How do I combine models trained on different data to increase classification accuracy?
You can try using a multi-input model. Here is a recent post with a similar discussion, with the required architecture defined in the answer.

Instead of combining the separate models, you can create a model which uses image and numerical data side by side. Keras allows you to use different types of data using multi input structure via functional API. And then you can combine them to create a single machine learning model. Basic idea is like this:



This image was taken from here, where you can find further details and the code implementation as well. Actually, the discussion in the introduction of that post is almost the same as your question: What to do when the inputs are:


  
  Numeric/continuous values, such as age, heart rate, blood pressure
  Categorical values, including gender and ethnicity 
  Image data, such as any MRI, X-ray, etc.
  


Also, section 5.1 of this blogpost details the same process.